CSCI498B/598B
Human-Centered Robotics
September 30, 2015
Histogram
• A histogram is a bar chart that shows how many
data points fit within a certain range
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Histogram
• That range is the bin width.
• The height of a rectangle is the frequency
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Histogram
• Histogram: In statistics, a histogram is a graphical representation of
the distribution of data.
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Key issues in histogram
• Dealing with noise
• Dealing with different number
of data instances
• Selecting hyper-parameters
• Dealing with data from multisources
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Key issues in histogram
• Dealing with noise
• Dealing with different number
of data instances
• Selecting hyper-parameters
•Dealing with data from multisources
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Histogram
• How to build a histogram?
 Show frequencies of a range of values by height of each bar
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Histogram
• How to build a histogram?
 Show frequencies of a range of values by height of each bar
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Histogram
• How to build a histogram?
 Show frequencies of a range of values by height of each bar
Noise?
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Histogram
• How to build a histogram?
 Show frequencies of a range of values by height of each bar
Noise?
Simply ignore the noisy data
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Histogram
• How to build a histogram?
 Show frequencies of a range of values by height of each bar
Noise?
Simply ignore the noisy data
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Histogram
• How to build a histogram?
 Show frequencies of a range of values by height of each bar
Noise?
Change boundary bin range
Use as a single bin
> 200
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Histogram
• How to build a histogram?
 Show frequencies of a range of values by height of each bar
Noise?
Change boundary bin range
Use as a single bin
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Key issues in histogram
• Dealing with noise
• Dealing with different number
of data instances
• Selecting hyper-parameters
•Dealing with data from multisources
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Histogram
• A histogram can be normalized displaying relative
frequencies.
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Histogram
• A histogram can be normalized displaying relative
frequencies.
• It then shows the proportion of data that fall into
each bin.
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Histogram
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Key issues in histogram
• Dealing with noise
• Dealing with different number
of data instances
• Selecting hyper-parameters
• Dealing with data from multisources
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Histogram
• Choose a user-defined number of bins
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Histogram
• Choose a user-defined number of bins
• Too many bins: bins too small (range too narrow)
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Histogram
• Choose a user-defined number of bins
• Too few bins: bins too large (range too wide)
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Key issues in histogram
• Dealing with noise
• Dealing with different number
of data instances
• Selecting hyper-parameters
• Dealing with data from multisources
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Histogram
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Key issues in histogram
• Dealing with noise
• Dealing with different number
of data instances
• Selecting hyper-parameters
• Dealing with data from multisources
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General Pipeline
1. Data acquisition
2. Representation construction
3. Pattern recognition (classification or clustering)
4. Decision making
5. Taking an action
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Skeleton-based representations
• How to encode spatio-temporal characteristics?
A simplest method is to use
HISTOGRAMS
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Skeleton-based representations
• Solution 1: Only use more descriptive joints
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Skeleton-based representations
• Solution 2: Compute additional features
𝒅
𝜽
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Skeleton-based representation
• Solution 2: Feature extraction - compute additional features
Representation: Histogram of Joint Position Differences
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Skeleton-based representation
• Solution 2: Feature extraction - compute additional features
Representation: Joint Movement Volume Features
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Skeleton-based representation
• Solution 2: Feature extraction - compute additional features
Representation: Joint Movement Volume Features
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Skeleton-based representation
• Solution 2: Feature extraction - compute additional features
Representation: Covariance of 3D Joints
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Skeleton-based representation
• Solution 2: Feature extraction - compute additional features
Representation: Covariance of 3D Joints
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Skeleton-based representation
• Solution 2: Feature extraction - compute additional features
Representation: Covariance of 3D Joints
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Skeleton-based representation
• Solution 2: Feature extraction - compute additional features
Representation: Covariance of 3D Joints
Temporal Pyramid:
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Skeleton-based representation
• Solution 2: Feature extraction - compute additional features
Representation: Histogram of Oriented Displacement
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Skeleton-based representation
• Solution 2: Feature extraction - compute additional features
Representation: Histogram of Oriented Displacement
Temporal pyramid also applies.
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